Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Gunnar Raetsch"'
Autor:
Patrick Forny, Ximena Bonilla, David Lamparter, Wenguang Shao, Tanja Plessl, Caroline Frei, Anna Bingisser, Sandra Goetze, Audrey van Drogen, Keith Harshman, Patrick G. A. Pedrioli, Cedric Howald, Martin Poms, Florian Traversi, Sarah Cherkaoui, Raphael J. Morscher, Luke Simmons, Merima Forny, Ioannis Xenarios, Ruedi Aebersold, Nicola Zamboni, Gunnar Raetsch, Emmanouil Dermitzakis, Bernd Wollscheid, Matthias R. Baumgartner, D. Sean Froese
Multi-layered omics approaches can help define relationships between genetic factors, biochemical processes and phenotypes thus extending research of inherited diseases beyond identifying their monogenic cause 1. We implemented a multi-layered omics
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31c66a499557bdb72e7a6a318d421208
https://doi.org/10.1101/2022.01.27.22269972
https://doi.org/10.1101/2022.01.27.22269972
MotivationDeep learning techniques have yielded tremendous progress in the field of computational biology over the last decade, however many of these techniques are opaque to the user. To provide interpretable results, methods have incorporated biolo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3c0d94c521889d4b9a397c394497b7a
https://doi.org/10.1101/2021.01.28.428664
https://doi.org/10.1101/2021.01.28.428664
Autor:
Gunnar Raetsch, Jun Liao, Manfred K. Warmuth, Santosh Putta, Michael Mathieson, Christian Lemmen
Publikováno v:
Journal of chemical information and computer sciences. 43(2)
We investigate the following data mining problem from computer-aided drug design: From a large collection of compounds, find those that bind to a target molecule in as few iterations of biochemical testing as possible. In each iteration a comparative
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24aaade471a9ad58e922db9dff93981d
https://pubmed.ncbi.nlm.nih.gov/12653536
https://pubmed.ncbi.nlm.nih.gov/12653536
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in
Conference
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Conference
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